December  2020, 10(4): 439-449. doi: 10.3934/naco.2020043

On box-constrained total least squares problem

LMIB of the Ministry of Education, School of Mathematical Sciences, Beihang University, Beijing, 100191, P. R. China

* Corresponding author: Y. Xia

Received  May 2020 Revised  September 2020 Published  September 2020

Fund Project: This research was supported by NSFC grants 11822103, 11571029, 11771056 and Beijing Natural Science Foundation Z180005

We study box-constrained total least squares problem (BTLS), which minimizes the ratio of two quadratic functions with lower and upper bounded constraints. We first prove that (BTLS) is NP-hard. Then we show that for fixed number of dimension, it is polynomially solvable. When the constraint box is centered at zero, a relative $ 4/7 $-approximate solution can be obtained in polynomial time based on SDP relaxation. For zero-centered and unit-box case, we show that the direct nontrivial least square relaxation could provide an absolute $ (n+1)/2 $-approximate solution. In the general case, we propose an enhanced SDP relaxation for (BTLS). Numerical results demonstrate significant improvements of the new relaxation.

Citation: Zhuoyi Xu, Yong Xia, Deren Han. On box-constrained total least squares problem. Numerical Algebra, Control & Optimization, 2020, 10 (4) : 439-449. doi: 10.3934/naco.2020043
References:
[1]

K. Anstreicher, Semidefinite programming versus the reformulation-linearization technique for nonconvex quadratically constrained quadratic programming, J. Global Optim., 43 (2009), 471-484.  doi: 10.1007/s10898-008-9372-0.  Google Scholar

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A. BeckA. Ben-Tal and M. Teboulle, Finding a global optimal solution for a quadratically constrained fractional quadratic problem with applications to the regularized total least squares, SIAM J. Matrix Anal. Appl., 28 (2006), 425-445.  doi: 10.1137/040616851.  Google Scholar

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A. Beck and M. Teboulle, On minimizing quadratically constrained ratio of two quadratic functions, J. Convex Anal., 17 (2010), 789-804.   Google Scholar

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A. Beck and D. Pan, A branch and bound algorithm for nonconvex quadratic optimization with ball and linear constraints, J. Global Optim., 69 (2017), 309-342.  doi: 10.1007/s10898-017-0521-1.  Google Scholar

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A. Ben-Tal and M. Teboulle, Hidden convexity in some nonconvex quadratically constrained quadratic programming, Math. Program., 72 (1996), 51-63.  doi: 10.1016/0025-5610(95)00020-8.  Google Scholar

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D. Bienstock and A. Michalka, Polynomial solvability of variants of the trust-region subproblem, in Proceedings of the 2014 Annual ACM-SIAM Symposium on Discrete Algorithms(eds. C. Chekuri), SODA, (2014), 380–390. doi: 10.1137/1.9781611973402.28.  Google Scholar

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S. Burer and K. Anstreicher, Second-order-cone constraints for extended trust-region problems, SIAM J. Optim., 23 (2013), 432-451.  doi: 10.1137/110826862.  Google Scholar

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A. Charnes and W. Cooper, Programming with linear fractional functionals, Nav. Res. Logist. Q., 9 (1962), 181-186.  doi: 10.1002/nav.3800090303.  Google Scholar

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CVX Research, Inc., CVX: Matlab software for disciplined convex programming, version 2.0., http://cvxr.com/cvx, April 2011. Google Scholar

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W. Dinkelbach, On nonlinear fractional programming, Manag. Sci., 13 (1967), 492-498.  doi: 10.1287/mnsc.13.7.492.  Google Scholar

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M. Garey and D. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. Freeman, San Francisco, 1979.  Google Scholar

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G. Golub and C. Loan, An analysis of the total least-squares problem, SIAM J. Numer. Anal., 17 (1980), 883-893.  doi: 10.1137/0717073.  Google Scholar

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M. Goemans and D. Williamson, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming, J. ACM, 42 (1995), 1115-1145.  doi: 10.1145/227683.227684.  Google Scholar

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Y. Hsia and R. L. Sheu, Trust region subproblem with a fixed number of additional linear inequality constraints has polynomial complexity, arXiv: 1312.1398, 2013 Google Scholar

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Z. LuoW. MaA. SoY. Ye and S. Zhang, Semidefinite relaxation of quadratic optimization problems, IEEE Signal Proc. Mag., 27 (2010), 20-34.   Google Scholar

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J. Martínez, Local minimizers of quadratic functions on Euclidean balls and spheres, SIAM J. Optim., 4 (1994), 159-176.  doi: 10.1137/0804009.  Google Scholar

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Y. Nesterov and A. Nemirovskii, Interior Point Polynomial Methods in Convex Programming: Theory and Algorithms, SIAM Publications, SIAM, Philadelphia, 1993. doi: 10.1137/1.9781611970791.  Google Scholar

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Y. Nesterov, Semidefinite relaxation and nonconvex quadratic optimization, Optim. Method Softw., 9 (1998), 141-160.  doi: 10.1080/10556789808805690.  Google Scholar

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Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, Kluwer Academic Publishers, 2003. doi: 10.1007/978-1-4419-8853-9.  Google Scholar

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P. Pardalos and A. Phillips, Global optimization of fractional programs, J. Global Optim., 1 (1991), 173-182.  doi: 10.1007/BF00119990.  Google Scholar

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J. Sturm and S. Zhang, On cones of nonnegative quadratic functions, Math. Oper. Res., 28 (2003), 246-267.  doi: 10.1287/moor.28.2.246.14485.  Google Scholar

[23]

Y. Xia, On minimizing the ratio of quadratic functions over an ellipsoid, Optimization, 64 (2015), 1097-1106.  doi: 10.1080/02331934.2013.840623.  Google Scholar

[24]

Y. Xia, A survey of hidden convex optimization, J. Oper. Res. Soc. China., 8 (2020), 1-28.  doi: 10.1007/s40305-019-00286-5.  Google Scholar

[25]

M. Yang and Y. Xia, On Lagrangian duality gap of quadratic fractional programming with a two-sided quadratic constraint, Optim. Lett., 14 (2020), 569–578., doi: 10.1007/s11590-018-1320-4.  Google Scholar

[26]

Y. Ye, Approximating quadratic programming with bound and quadratic constraints, Math. Program., 84 (1999), 219-226.  doi: 10.1007/s10107980012a.  Google Scholar

[27]

Y. Ye and S. Zhang, New results on quadratic minimization, SIAM J. Optim., 14 (2003), 245-267.  doi: 10.1137/S105262340139001X.  Google Scholar

show all references

References:
[1]

K. Anstreicher, Semidefinite programming versus the reformulation-linearization technique for nonconvex quadratically constrained quadratic programming, J. Global Optim., 43 (2009), 471-484.  doi: 10.1007/s10898-008-9372-0.  Google Scholar

[2]

A. BeckA. Ben-Tal and M. Teboulle, Finding a global optimal solution for a quadratically constrained fractional quadratic problem with applications to the regularized total least squares, SIAM J. Matrix Anal. Appl., 28 (2006), 425-445.  doi: 10.1137/040616851.  Google Scholar

[3]

A. Beck and M. Teboulle, A convex optimization approach for minimizing the ratio of indefinite quadratic functions over an ellipsoid, Math. Program., 118 (2009), 13-35.  doi: 10.1007/s10107-007-0181-x.  Google Scholar

[4]

A. Beck and M. Teboulle, On minimizing quadratically constrained ratio of two quadratic functions, J. Convex Anal., 17 (2010), 789-804.   Google Scholar

[5]

A. Beck and D. Pan, A branch and bound algorithm for nonconvex quadratic optimization with ball and linear constraints, J. Global Optim., 69 (2017), 309-342.  doi: 10.1007/s10898-017-0521-1.  Google Scholar

[6]

A. Ben-Tal and M. Teboulle, Hidden convexity in some nonconvex quadratically constrained quadratic programming, Math. Program., 72 (1996), 51-63.  doi: 10.1016/0025-5610(95)00020-8.  Google Scholar

[7]

D. Bienstock and A. Michalka, Polynomial solvability of variants of the trust-region subproblem, in Proceedings of the 2014 Annual ACM-SIAM Symposium on Discrete Algorithms(eds. C. Chekuri), SODA, (2014), 380–390. doi: 10.1137/1.9781611973402.28.  Google Scholar

[8]

S. Burer and K. Anstreicher, Second-order-cone constraints for extended trust-region problems, SIAM J. Optim., 23 (2013), 432-451.  doi: 10.1137/110826862.  Google Scholar

[9]

A. Charnes and W. Cooper, Programming with linear fractional functionals, Nav. Res. Logist. Q., 9 (1962), 181-186.  doi: 10.1002/nav.3800090303.  Google Scholar

[10]

CVX Research, Inc., CVX: Matlab software for disciplined convex programming, version 2.0., http://cvxr.com/cvx, April 2011. Google Scholar

[11]

W. Dinkelbach, On nonlinear fractional programming, Manag. Sci., 13 (1967), 492-498.  doi: 10.1287/mnsc.13.7.492.  Google Scholar

[12]

M. Garey and D. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. Freeman, San Francisco, 1979.  Google Scholar

[13]

G. Golub and C. Loan, An analysis of the total least-squares problem, SIAM J. Numer. Anal., 17 (1980), 883-893.  doi: 10.1137/0717073.  Google Scholar

[14]

M. Goemans and D. Williamson, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming, J. ACM, 42 (1995), 1115-1145.  doi: 10.1145/227683.227684.  Google Scholar

[15]

Y. Hsia and R. L. Sheu, Trust region subproblem with a fixed number of additional linear inequality constraints has polynomial complexity, arXiv: 1312.1398, 2013 Google Scholar

[16]

Z. LuoW. MaA. SoY. Ye and S. Zhang, Semidefinite relaxation of quadratic optimization problems, IEEE Signal Proc. Mag., 27 (2010), 20-34.   Google Scholar

[17]

J. Martínez, Local minimizers of quadratic functions on Euclidean balls and spheres, SIAM J. Optim., 4 (1994), 159-176.  doi: 10.1137/0804009.  Google Scholar

[18]

Y. Nesterov and A. Nemirovskii, Interior Point Polynomial Methods in Convex Programming: Theory and Algorithms, SIAM Publications, SIAM, Philadelphia, 1993. doi: 10.1137/1.9781611970791.  Google Scholar

[19]

Y. Nesterov, Semidefinite relaxation and nonconvex quadratic optimization, Optim. Method Softw., 9 (1998), 141-160.  doi: 10.1080/10556789808805690.  Google Scholar

[20]

Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, Kluwer Academic Publishers, 2003. doi: 10.1007/978-1-4419-8853-9.  Google Scholar

[21]

P. Pardalos and A. Phillips, Global optimization of fractional programs, J. Global Optim., 1 (1991), 173-182.  doi: 10.1007/BF00119990.  Google Scholar

[22]

J. Sturm and S. Zhang, On cones of nonnegative quadratic functions, Math. Oper. Res., 28 (2003), 246-267.  doi: 10.1287/moor.28.2.246.14485.  Google Scholar

[23]

Y. Xia, On minimizing the ratio of quadratic functions over an ellipsoid, Optimization, 64 (2015), 1097-1106.  doi: 10.1080/02331934.2013.840623.  Google Scholar

[24]

Y. Xia, A survey of hidden convex optimization, J. Oper. Res. Soc. China., 8 (2020), 1-28.  doi: 10.1007/s40305-019-00286-5.  Google Scholar

[25]

M. Yang and Y. Xia, On Lagrangian duality gap of quadratic fractional programming with a two-sided quadratic constraint, Optim. Lett., 14 (2020), 569–578., doi: 10.1007/s11590-018-1320-4.  Google Scholar

[26]

Y. Ye, Approximating quadratic programming with bound and quadratic constraints, Math. Program., 84 (1999), 219-226.  doi: 10.1007/s10107980012a.  Google Scholar

[27]

Y. Ye and S. Zhang, New results on quadratic minimization, SIAM J. Optim., 14 (2003), 245-267.  doi: 10.1137/S105262340139001X.  Google Scholar

Table 1.  Numerical comparison of lower bounds
$ (m,n) $ $ l=0,u=50 $ $ l=0,u=100 $ $ l=0,u=255 $
$ v $(SDP) $ v $(ISDP) $ v $(SDP) $ v $(ISDP) $ v $(SDP) $ v $(ISDP)
(15, 10) 0.1847 0.1884 0.1742 0.1760 0.1675 0.1683
(50, 25) 0.4530 0.5043 0.4137 0.4425 0.3893 0.4011
(75, 50) 0.4732 0.5302 0.4214 0.4546 0.3893 0.4033
(100, 75) 0.3218 0.3825 0.2501 0.2875 0.2057 0.2229
(125,100) 0.4034 0.4805 0.3010 0.3541 0.2372 0.2646
(150,125) 0.3664 0.4496 0.2403 0.3028 0.1622 0.2163
$ (m,n) $ $ l=0,u=50 $ $ l=0,u=100 $ $ l=0,u=255 $
$ v $(SDP) $ v $(ISDP) $ v $(SDP) $ v $(ISDP) $ v $(SDP) $ v $(ISDP)
(15, 10) 0.1847 0.1884 0.1742 0.1760 0.1675 0.1683
(50, 25) 0.4530 0.5043 0.4137 0.4425 0.3893 0.4011
(75, 50) 0.4732 0.5302 0.4214 0.4546 0.3893 0.4033
(100, 75) 0.3218 0.3825 0.2501 0.2875 0.2057 0.2229
(125,100) 0.4034 0.4805 0.3010 0.3541 0.2372 0.2646
(150,125) 0.3664 0.4496 0.2403 0.3028 0.1622 0.2163
Table 2.  Numerical comparison of CPU time
$ (m,n) $ $ l=0,u=50 $ $ l=0,u=100 $ $ l=0,u=255 $
$ t $(SDP) $ t $(ISDP) $ t $(SDP) $ t $(ISDP) $ t $(SDP) $ t $(ISDP)
(15, 10) 0.69 2.70 0.34 1.35 0.41 1.41
(50, 25) 0.94 8.34 0.40 4.36 0.42 4.67
(75, 50) 1.44 36.81 0.70 19.16 0.68 20.76
(100, 75) 2.33 109.05 1.27 66.26 1.26 70.89
(125,100) 4.92 259.68 2.91 176.77 2.87 239.28
(150,125) 9.99 686.35 6.65 564.68 6.11 647.24
$ (m,n) $ $ l=0,u=50 $ $ l=0,u=100 $ $ l=0,u=255 $
$ t $(SDP) $ t $(ISDP) $ t $(SDP) $ t $(ISDP) $ t $(SDP) $ t $(ISDP)
(15, 10) 0.69 2.70 0.34 1.35 0.41 1.41
(50, 25) 0.94 8.34 0.40 4.36 0.42 4.67
(75, 50) 1.44 36.81 0.70 19.16 0.68 20.76
(100, 75) 2.33 109.05 1.27 66.26 1.26 70.89
(125,100) 4.92 259.68 2.91 176.77 2.87 239.28
(150,125) 9.99 686.35 6.65 564.68 6.11 647.24
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